3D PET/CT Tumor Co-Segmentation Based on Background Subtraction Hybrid Active Contour Model

被引:1
作者
Li, Laquan [1 ,2 ]
Jiang, Chuangbo [1 ]
Wang, Patrick Shen-Pei [3 ]
Zheng, Shenhai [2 ,4 ]
机构
[1] Chongqing Univ Posts & Telecommun, Sch Sci, Chongqing 400065, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Coll Comp Sci & Technol, Chongqing 400065, Peoples R China
[3] Northeastern Univ, Coll Comp & Informat Sci, Boston, MA 02115 USA
[4] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China
关键词
Tumor co-segmentation; active contour model; level set; AOS format; PET/CT; IMAGES; EFFICIENT;
D O I
10.1142/S0218001423570069
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Accurate tumor segmentation in medical images plays an important role in clinical diagnosis and disease analysis. However, medical images usually have great complexity, such as low contrast of computed tomography (CT) or low spatial resolution of positron emission tomography (PET). In the actual radiotherapy plan, multimodal imaging technology, such as PET/CT, is often used. PET images provide basic metabolic information and CT images provide anatomical details. In this paper, we propose a 3D PET/CT tumor co-segmentation framework based on active contour model. First, a new edge stop function (ESF) based on PET image and CT image is defined, which combines the grayscale standard deviation information of the image and is more effective for blurry medical image edges. Second, we propose a background subtraction model to solve the problem of uneven grayscale level in medical images. Apart from that, the calculation format adopts the level set algorithm based on the additive operator splitting (AOS) format. The solution is unconditionally stable and eliminates the dependence on time step size. Experimental results on a dataset of 50 pairs of PET/CT images of non-small cell lung cancer patients show that the proposed method has a good performance for tumor segmentation.
引用
收藏
页数:25
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